The ability to utilize biometric data (e.g., fingerprints, facial features, iris, etc.) for identification purposes is an important task in many fields. Because of the numerous applications, technologies, and biometric data types that are utilized, the quantity and magnitude of biometric databases continue to increase in size and scope.
Unfortunately, larger database sizes typically translate into slower searching speeds and higher error rates when trying to identify a match for an inputted sample. For instance, if every United States citizen enrolled all 10 fingerprints, the national database would exceed 3 billion samples.
Technological advances have increased at least some processing within the biometric identification problem. For example, automated fingerprint systems are generally decomposed into fingerprint capture, feature extraction, file partitioning or binning, a prescreen matcher, a secondary matcher and decision logic. Fingerprint identification is a highly separated process such that a matcher and a templatizer algorithm can be placed on separate computing nodes. The massive parallelization enables a high matching and templitizing velocity. Numerous feature extraction, pattern recognition and template matching algorithms have been developed and analyzed with respect to speed and a receive operator curve. Techniques such as file partitioning, indexing or binning algorithms reduce the search space of a biometric database.
The problems associated with biometric data processing however become even more acute when multiple biometrics (e.g., fingerprints, iris, handwriting, etc.) are utilized as part of an identification process. In particular, larger databases, different types of data structures, different processes, etc., must be accommodated within a single system. Currently, there are limited solutions for providing such an infrastructure. Accordingly, a need exists for a biometric infrastructure that can effectively process different biometric features in a comprehensive manner.